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Hybrid algorithms for placement of virtual machines across geo-separated data centers

  • Fernando StefanelloEmail author
  • Vaneet Aggarwal
  • Luciana S. Buriol
  • Mauricio G. C. Resende
Article
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Abstract

Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard generalized quadratic assignment problem (GQAP). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

Keywords

Combinatorial optimization Cloud computing Biased random-key genetic algorithm GRASP Path-relinking 

Notes

Acknowledgements

This work has been partially supported by CAPES, CNPq Project 462425/2014-2, PRH PB-217 Petrobras S.A. from Brazil. We thank to K. Joshi at AT&T Labs-Research for many helpful discussions. The work of Vaneet Aggarwal and Mauricio G.C. Resende was done when both were employed at AT&T Labs Research.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Instituto de InformáticaFederal University of Rio Grande do SulPorto AlegreBrazil
  2. 2.School of Industrial Engineering, School of Electrical and Computer EngineeringPurdue UniversityWest LafayetteUSA
  3. 3.Amazon.comSeattleUSA
  4. 4.Industrial and Systems EngineeringUniversity of WashingtonSeattleUSA

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